Clinical Projects A. Assessing the potential risk in drug prescriptions during pregnancy Eighty percent of the pregnant women in the US have at least one drug prescription during their pregnancies. In 2015, the Food and Drug Administration (FDA) introduced new drug labeling regulations, with narrative summaries describing the risks and supporting evidence of drug prescriptions during pregnancy. We did an analysis that assessed the potential risks, with respect to the new FDA standard using claims data of 159.7M patients from 2003-2014. This analysis was a project within the Innovation in Medical Evidence Development and Surveillance, created by the Reagan-Udall Foundation for the FDA. We identified pregnant women by procedure codes for delivery, and extracted prescriptions 270 days before delivery. We used the RxNorm API to relate drugs from claims data to the reference. Of the 15M systemic drugs prescribed to 3M pregnant women, 93% were covered by our classification scheme. The distribution among 6 broad categories was: compatible with pregnancy or probably compatible (41.2%), low risk (16.2%), moderate risk (39.3%), high risk and contraindicated (3.29%). Interestingly, a majority of the risk assessments was supported by evidence from human data. B. Association between use of Androgen Deprivation Therapy (ADT) and incidence of Alzheimers disease (AD) or dementia among patients diagnosed with prostate cancer (PCa) ADT is the first line of treatment for metastatic PCa and the main treatment for advanced PCa. Two prior studies reported about twofold risk of future AD or dementia among ADT treated patients. We identified 1.2M patients with PCa from 20012014 Medicare database and defined treatments for PCa: ADT, chemotherapy, radiation therapy, and prostatectomy using HCPCS codes. We analyzed risk of AD or dementia using a competing risk regression. As opposed to the prior studies, ADT was not associated with at increased risk of AD and had only miniscule (1%) risk for dementia (HR=1.01). This study was selected for the featured article in the Journal of Clinical Oncology. C. Association between use of Cardiovascular Medications and incidence of dementia Antihypertensive and lipid-lowering medications (statins) are most commonly prescribed drugs in Medicare. We utilized Part D prescription drug records for 600K Medicare beneficiaries to assess how the exposure to antihypertensive drugs or statins is associated with the incidence of dementia. Using 2007-2015 Prescription Drug records, we separately defined use of drugs: Statins, -blockers, calcium-channel blockers, diuretics, renin-angiotensin system (RAS) inhibitors and proton pump inhibitors. From a competing risk regression with time-dependent covariates, we found diuretics and RAS inhibitors were beneficial, but other drugs (Statins, -blockers, calcium-channel blockers and PPIs) were not to prevent/delay dementia. The risk reduction was, however, not as large as previously reported. This work was presented at the NLM 2017 Informatics Training Conference in San Diego, CA and accepted for the AMIA 2017 Annual Symposium. D. Metformin and longevity In the last decade, research on aging found that metformin use was associated with a reduction in cognitive decline and a longer survival of among diabetics compared to those treated with other oral agents. We analyzed 157K Medicare beneficiaries with type 2 diabetes using Cox regression with time-varying covariates. Controlling for use of insulin, sulfonylurea, GLP-1 analogues, thiazolidinedione, DPP-4 inhibitor and other glucose lowering drugs, anti-hypertensive drugs, statins, socio-demographics, and presence of 57 chronic conditions in time-varying manner, we found metformin was associated with a decreased risk of all-cause mortality (HR=0.72), but this beneficial effect was not unique to metformin. GLP-1 analogues, antihypertensive drugs and statins had even greater risk reduction effect than metformin. E. Proton Pump Inhibitors (PPIs) and mortality Proton pump inhibitors (PPIs) have been associated with increases in the incidence of pneumonia, C Decile infection and osteoporosis /fractures, probable chronic renal failure and cardiac events. Xie at al reported that PPIs could also increase the risk of death using Veterans Affairs (VA) data. From 2007-2015 Medicare data, we identified 581K Medicare beneficiaries and defined a set of covariates: use of PPIs and H2 blockers, admission to intensive care units/inpatient hospitals, socio-demographics, presence of 58 chronic conditions and treated them as time-varying covariates in a Cox regression. In the unadjusted analysis, we found that PPI treated patients died more (19.1 vs 11.1 death per 1000 person-years). In contrast to the results Xie et als study, however, the relative risk of death among PPI treated patients were not higher (HR=0.97) after adjusting for covariates. F. Sepsis and Disseminated Intravascular Coagulation Easy availability of clinical databases to researchers is an ongoing challenge. The MIMIC2 database (that originated at MIT), allows access to de-identified data on intensive care unit patients. The MIMIC database is unique in pioneering relatively streamlined access to clinical data to researchers. We have used this database to study Disseminated Intravascular Coagulation (DIC) condition in 2,257 patients with sepsis using the MIMIC2 database 5. G. Comparing the performance of commercial drug knowledge bases (KBs) in detecting drug-drug interactions (DDIs) DDIs are a significant cause of adverse drug events and hospital admissions. Physicians rely on clinical decision support systems to alert them of potential interactions. These systems are often based on a drug interaction KB from a single vendor. Previous studies have shown significant variation between these KBs. To compare the performance of different commercial KBs in a simulated clinical context, we applied three commercial KBs to a large data set of patient prescription data (acquired from Symphony Health) to evaluate the overall alert level and coverage of a list of clinically significant drug interactions. Informatics infrastructure for large clinical databases H. Data quality In a research consortium data quality study, we have extended the OHDSI data characterization tools (called Achilles) and OHDSI data quality assessment (DQA) tool (called Achilles Heel) with new data quality rules. We conducted an evaluation study of this tool and a qualitative survey about DQA at 7 sites. This was published in eGEMs journal https://www.ncbi.nlm.nih.gov/pubmed/28154833. We have created a methodology for using OHDSI Achilles tool pre-computed measures to detect temporal trends in drug, procedure, diagnosis data and laboratory data. I. Clinical Data Representation We explored optimal representation of drug data within the Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM). J. Research Data representation We collaborated on research that used OMOP Common Data Model representation and a similarity method to identify patients potentially eligible for a clinical trial